An algorithmic framework for synthetic cost-aware decision making in molecular design

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Jenna C. Fromer, Connor W. Coley
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引用次数: 0

Abstract

Small molecules exhibiting desirable property profiles are often discovered through an iterative process of designing, synthesizing and testing sets of molecules. The selection of molecules to synthesize from all possible candidates is a complex decision-making process that typically relies on expert chemist intuition. Here we propose a quantitative decision-making framework, SPARROW, that prioritizes molecules for evaluation by balancing expected information gain and synthetic cost. SPARROW integrates molecular design, property prediction and retrosynthetic planning to balance the utility of testing a molecule with the cost of batch synthesis. We demonstrate, through three case studies, that the developed algorithm captures the non-additive costs inherent to batch synthesis, leverages common reaction steps and intermediates, and scales to hundreds of molecules. The downselection of compounds for synthesis is a key challenge in molecular design cycles that typically relies on expert chemist intuition. Fromer and Coley propose a cost-aware method to automatically select compounds and synthetic routes.

Abstract Image

分子设计中合成成本感知决策的算法框架。
表现出理想特性的小分子通常是通过设计、合成和测试成套分子的反复过程发现的。从所有可能的候选分子中选择要合成的分子是一个复杂的决策过程,通常依赖于化学家的直觉。在此,我们提出了一个定量决策框架 SPARROW,该框架通过平衡预期信息增益和合成成本来确定评估分子的优先次序。SPARROW 整合了分子设计、性质预测和逆合成规划,以平衡测试分子的效用和批量合成的成本。我们通过三个案例研究证明,所开发的算法能够捕捉到批量合成固有的非加成成本,利用常见的反应步骤和中间体,并可扩展到数百个分子。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
11.70
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0.00%
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